Van der Waals polarity-engineered 3D integration of 2D complementary logic.

Vertical three-dimensional integration of two-dimensional (2D) semiconductors holds great promise, as it offers the possibility to scale up logic layers in the z axis1-3. Indeed, vertical complementary field-effect transistors (CFETs) built with such mixed-dimensional heterostructures4,5, as well as hetero-2D layers with different carrier types6-8, have been demonstrated recently. However, so far, the lack of a controllable doping scheme (especially p-doped WSe2 (refs. 9-17) and MoS2 (refs. 11,18-28)) in 2D semiconductors, preferably in a stable and non-destructive manner, has greatly impeded the bottom-up scaling of complementary logic circuitries. Here we show that, by bringing transition metal dichalcogenides, such as MoS2, atop a van der Waals (vdW) antiferromagnetic insulator chromium oxychloride (CrOCl), the carrier polarity in MoS2 can be readily reconfigured from n- to p-type via strong vdW interfacial coupling. The consequential band alignment yields transistors with room-temperature hole mobilities up to approximately 425 cm2 V-1 s-1, on/off ratios reaching 106 and air-stable performance for over one year. Based on this approach, vertically constructed complementary logic, including inverters with 6 vdW layers, NANDs with 14 vdW layers and SRAMs with 14 vdW layers, are further demonstrated. Our findings of polarity-engineered p- and n-type 2D semiconductor channels with and without vdW intercalation are robust and universal to various materials and thus may throw light on future three-dimensional vertically integrated circuits based on 2D logic gates.

Generative models for protein sequence modeling: recent advances and future directions.

The widespread adoption of high-throughput omics technologies has exponentially increased the amount of protein sequence data involved in many salient disease pathways and their respective therapeutics and diagnostics. Despite the availability of large-scale sequence data, the lack of experimental fitness annotations underpins the need for self-supervised and unsupervised machine learning (ML) methods. These techniques leverage the meaningful features encoded in abundant unlabeled sequences to accomplish complex protein engineering tasks. Proficiency in the rapidly evolving fields of protein engineering and generative AI is required to realize the full potential of ML models as a tool for protein fitness landscape navigation. Here, we support this work by (i) providing an overview of the architecture and mathematical details of the most successful ML models applicable to sequence data (e.g. variational autoencoders, autoregressive models, generative adversarial neural networks, and diffusion models), (ii) guiding how to effectively implement these models on protein sequence data to predict fitness or generate high-fitness sequences and (iii) highlighting several successful studies that implement these techniques in protein engineering (from paratope regions and subcellular localization prediction to high-fitness sequences and protein design rules generation). By providing a comprehensive survey of model details, novel architecture developments, comparisons of model applications, and current challenges, this study intends to provide structured guidance and robust framework for delivering a prospective outlook in the ML-driven protein engineering field.

Precise Surface Profiling at the Nanoscale Enabled by Deep Learning.

Surface topography, or height profile, is a critical property for various micro- and nanostructured materials and devices, as well as biological systems. At the nanoscale, atomic force microscopy (AFM) is the tool of choice for surface profiling due to its capability to noninvasively map the topography of almost all types of samples. However, this method suffers from one drawback: the convolution of the nanoprobe's shape in the height profile of the samples, which is especially severe for sharp protrusion features. Here, we report a deep learning (DL) approach to overcome this limit. Adopting an image-to-image translation methodology, we use data sets of tip-convoluted and deconvoluted image pairs to train an encoder-decoder based deep convolutional neural network. The trained network successfully removes the tip convolution from AFM topographic images of various nanocorrugated surfaces and recovers the true, precise 3D height profiles of these samples.

Electro-Induced Phase Transformation of a Conductive Metal-Organic Framework Film for Nonlinear Optical Switching.

Achieving metal-organic frameworks (MOFs) with nonlinear optical (NLO) switching is profoundly important. Herein, the conductive MOFs Cu-TCNQ phase I (Ph-I) and phase II (Ph-II) films were prepared using the liquid-phase-epitaxial layer-by-layer spin-coating method and steam heating method, respectively. Electronic experiments showed that the Ph-II film could be changed into the Ph-I film under an applied electric field. The third-order NLO results revealed that the Ph-I film had a third-order nonlinear reverse saturation absorption (RSA) response and the Ph-II film displayed a third-order nonlinear saturation absorption (SA) response. With increases in the heating time and applied voltage, the third-order NLO response realized the reversible transition between SA and RSA. The theoretical calculations indicated that Ph-I possessed more interlayer charge transfer, resulting in a third-order nonlinear RSA response that was stronger than that of Ph-II. This work applies phase-transformed MOFs to third-order NLO switching and provides new insights into the nonlinear photoelectric applications of MOFs.

Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

Pore-Forming Toxin-Driven Recovery of Peroxidase-Mimicking Activity in Biomass Channels for Label-Free Electrochemical Bacteria Sensing.

The development of sensitive, selective, and rapid methods to detect bacteria in complex media is essential to ensuring human health. Virulence factors, particularly pore-forming toxins (PFTs) secreted by pathogenic bacteria, play a crucial role in bacterial diseases and serve as indicators of disease severity. In this study, a nanochannel-based label-free electrochemical sensing platform was developed for the detection of specific pathogenic bacteria based on their secreted PFTs. In this design, wood substrate channels were functionalized with a Fe-based metal-organic framework (FeMOF) and then protected with a layer of phosphatidylcholine (PC)-based phospholipid membrane (PM) that serves as a peroxidase mimetic and a channel gatekeeper, respectively. Using Staphylococcus aureus (S. aureus) as the model bacteria, the PC-specific PFTs secreted by S. aureus perforate the PM layer. Now exposed to the FeMOF, uncharged 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) molecules in the electrolyte undergo oxidation to cationic products (ABTS•+). The measured transmembrane ionic current indicates the presence of S. aureus and methicillin-resistant S. aureus (MRSA) with a low detection limit of 3 cfu mL-1. Besides excellent specificity, this sensing approach exhibits satisfactory performance for the detection of target bacteria in the complex media of food.

Mandelonitrile lyase MDL2-mediated regulation of seed amygdalin and oil accumulation of Prunus Sibirica.

BACKGROUND: The Prunus sibirica seeds with rich oils has great utilization, but contain amygdalin that can be hydrolyzed to release toxic HCN. Thus, how to effectively reduce seed amygdalin content of P. sibirica is an interesting question. Mandelonitrile is known as one key intermediate of amygdalin metabolism, but which mandelonitrile lyase (MDL) family member essential for its dissociation destined to low amygdalin accumulation in P. sibirica seeds still remains enigmatic. An integration of our recent 454 RNA-seq data, amygdalin and mandelonitrile content detection, qRT-PCR analysis and function determination is described as a critical attempt to determine key MDL and to highlight its function in governing mandelonitrile catabolism with low amygdalin accumulation in Prunus sibirica seeds for better developing edible oil and biodiesel in China. RESULTS: To identify key MDL and to unravel its function in governing seed mandelonitrile catabolism with low amygdalin accumulation in P. sibirica. Global identification of mandelonitrile catabolism-associated MDLs, integrated with the across-accessions/developing stages association of accumulative amount of amygdalin and mandelonitrile with transcriptional level of MDLs was performed on P. sibirica seeds of 5 accessions to determine crucial MDL2 for seed mandelonitrile catabolism of P. sibirica. MDL2 gene was cloned from the seeds of P. sibirica, and yeast eukaryotic expression revealed an ability of MDL2 to specifically catalyze the dissociation of mandelonitrile with the ideal values of Km (0.22 mM) and Vmax (178.57 U/mg). A combination of overexpression and mutation was conducted in Arabidopsis. Overexpression of PsMDL2 decreased seed mandelonitrile content with an increase of oil accumulation, upregulated transcript of mandelonitrile metabolic enzymes and oil synthesis enzymes (involving FA biosynthesis and TAG assembly), but exhibited an opposite situation in mdl2 mutant, revealing a role of PsMDL2-mediated regulation in seed amygdalin and oil biosynthesis. The PsMDL2 gene has shown as key molecular target for bioengineering high seed oil production with low amygdalin in oilseed plants. CONCLUSIONS: This work presents the first integrated assay of genome-wide identification of mandelonitrile catabolism-related MDLs and the comparative association of transcriptional level of MDLs with accumulative amount of amygdalin and mandelonitrile in the seeds across different germplasms and developmental periods of P. sibirica to determine MDL2 for mandelonitrile dissociation, and an effective combination of PsMDL2 expression and mutation, oil and mandelonitrile content detection and qRT-PCR assay was performed to unravel a mechanism of PsMDL2 for controlling amygdalin and oil production in P. sibirica seeds. These findings could offer new bioengineering strategy for high oil production with low amygdalin in oil plants.

NaNO3-Promoted MgO-Based Adsorbents Prepared from Bischofite for CO2 Capture: Experimental and Density Functional Theory Study.

MgO has broad application potential in CO2 capture at intermedium temperatures. In this paper, the effects of NaNO3 doping on the properties of MgO prepared by using waste bischofite as the raw material were investigated to improve the performance of the CO2 capture. MgO-doped NaNO3 exhibited excellent CO2 capture performance at 320 °C with a maximum adsorption capacity of 36.62 wt %. MgO-doped NaNO3 has good cycling stability after 10 adsorption-desorption cycle experiments. In addition, CO2 adsorption on pure MgO and MgO-NaNO3 surfaces was investigated in accordance with density functional theory. Calculation results show that doping with NaNO3 allows more electrons to be transferred from the MgO substrate to the CO2 molecule. MgO-doped NaNO3 can lead to an increase in adsorption energy, resulting in a more stable structure after adsorption and thereby promoting adsorption. The result of this study provides an effective method for the comprehensive utilization of salt lake resources.

The migrasome as a developmental learning paradigm in cell biology.

Migrasome is a newly discovered organelle composed of small vesicular structures enclosed in membrane structures. Since its discovery in 2014, migrasome has attracted increasing attention in cell biology due to its critical role in multiple disease processes. Its pivotal role in various disease processes, including cell migration, intercellular communication, removal of damaged mitochondria, embryogenesis localization, immune cell chemotaxis, and virus transmission, underscores its significance in biological systems. With research on migrasome steadily increasing, it becomes a unique resource for undergraduate cell biology education. For deeper understanding of migrasome, we applied a bibliometric approach. Here we conducted a comprehensive analysis of migrasome research by retrieving relevant literature from databases such as Web of Science, Scopus, and PubMed using the keywords "migrasome" or "migrasomes." Employing CiteSpace software and Prism, we analyzed annual publication trends, identified core authors and institutions, assessed national contributions, examined keywords, and scrutinized highly cited literature related to migrasome research. This study presents a comprehensive overview of migrasome research, elucidating its literature characteristics, key contributors, research hotspots, and emerging trends. By shedding light on the current status and future trajectories of migrasome research, we aim to provide valuable insights for teachers in cell biology education. We propose for the integration of migrasome research into undergraduate curricula to enhance the understanding of cell biology among premedical, medical, and biomedical students, thereby fostering a deeper appreciation for the intricate mechanisms governing cellular behavior and disease processes.

Coordination-Induced Symmetry Breaking on Metal-Porphyrinic Framework Thin Films for Enhanced Nonlinear Optical Limiting.

Structural asymmetry affecting the nonlinear optics (NLO) of metal-organic frameworks (MOFs) is very important in fundamentals and applications but is still a challenge. Herein we develop a series of indium-porphyrinic framework (InTCPP) thin films and provide the first study on the coordination-induced symmetry breaking on their third-order NLO. The continuous and oriented InTCPP(H2) thin films were grown on quartz substrates and then postcoordinated with different cations (Fe2+ or Fe3+Cl-) in InTCPP(H2) (named InTCPP(Fe2+) and InTCPP(Fe3+Cl-)). The third-order NLO results reveal the Fe2+ and Fe3+Cl- coordinated InTCPP thin films have substantially enhanced NLO performance. Moreover, InTCPP(Fe3+Cl-) thin films cause symmetry breaking of microstructures, resulting in a 3-fold increase in the nonlinear absorption coefficient (up to 6.35 × 10-6 m/W) compared to InTCPP(Fe2+). This work not only develops a series of nonlinear optical MOF thin films but also provides new insight into symmetry breaking on MOFs for nonlinear optoelectronic applications.

Designed Synthesis of an Aluminum Molecular Ring Based Rotaxane and Polyrotaxane.

Mechanically interlocked molecules, such as rotaxanes, have drawn significant attention within supramolecular chemistry. Although a variety of macrocycles have been thoroughly explored in rotaxane synthesis, metal-organic macrocycles remain relatively under-investigated. Aluminum molecular rings, with their inner cavities and numerous binding sites, present a promising option for constructing rotaxanes. Here, we introduce an innovative "ring-donor···axle-acceptor" motif utilizing Al8 molecular rings, enabling the stepwise assembly of molecules, complexes, and polymers through tailored coordination chemistry. This novel approach can not only be applied to macrocycle-based systems like catenanes but also enhance specific functionalities progressively.

Improved Sensitivity and Selectivity of Glutathione Detection through Target-Driven Electron Donor Generation in Photoelectrochemical Electrodes.

Glutathione (GSH) plays a vital role in many physiological processes, and its abnormal levels have been found to be associated with several diseases. In contrast to traditional methods using electron donor-containing electrolytes for photoelectrochemical (PEC) sensing, in this study, a target-driven electron donor generation in a PEC electrode was developed to detect GSH. Using well-aligned TiO2 nanotube arrays (TNTs) as the PEC substrate, mesoporous MIL-125(Ti) was grown in the TNTs through an in situ solvothermal method and subsequent two-step annealing treatment. The accommodation capacity of mesoporous MIL-125(Ti) allows a well loading of cystine and Pt nanoclusters (NCs). Taking advantage of the specific cleavage ability of disulfide bonds by GSH, cystine was converted to cysteine, which served as the electron donor for the PEC process. Benefiting from the confinement effect of mesoporous MIL-125(Ti), cysteine was effectively oxidized to cysteine sulfinic acid by the photogenerated holes. Importantly, the highly active Pt NCs decorated in the mesopores not only improved the charge transfer but also accelerated the above oxidation reaction. The synergistic effect of these factors enabled the efficient separation of the photogenerated electron-hole pairs, which induced a significant photocurrent increase and in turn led to the high-sensitivity detection of GSH. Consequently, the proposed PEC biosensor exhibited excellent performance in the detection of GSH in serum specimens. The target-driven electron donor generation designed in this study might open a new route for developing sensitive and selective PEC biosensors with application in complex biological environments.

Improving the regeneration rate of deep lethal mutant protoplasts by fusion to promote efficient L-lysine fermentation.

BACKGROUND: L-lysine is widely used for feed and special diet products. The transformation of fermentation strains plays a decisive role in the development of these industries. Based on the mutation breeding theory and metabolic engineering methods, this study aimed to improve the regeneration rate of high-lethality protoplasts by combining multiple mutagenesis and homologous cell fusion techniques to efficiently concentrate multiple dominant mutations and optimize the L-lysine production strain Escherichia coli QDW. RESULTS: In order to obtain the best protoplasts, the optimal enzymolysis time was selected as 4 h. The optimal lysozyme concentration was estimated at 0.8 mg/mL, because the protoplast formation rate and regeneration rate reached 90% and 30%, respectively, and their product reached the maximum. In this study, it was necessary that UV mutagenesis be excessive to obtain an expanded mutation library. For high lethality protoplasts, under the premise of minimal influence on its recovery, the optimal time for UV mutagenesis of protoplasts was 7 min, and the optimal time for thermal inactivation of protoplasts at 85 ℃ was 30 min. After homologous fusion, four fusion strains of E. coli were obtained, and their stability was analyzed by flow cytometry. The L-lysine yield of QDW-UH3 increased by 7.2% compared with that of QDW in a fermentation experiment, which promoted the expression of key enzymes in L-lysine synthesis, indicating that the combination of ultraviolet mutagenic breeding and protoplast fusion technology improved the acid-production level of the fusion strain. CONCLUSION: This method provides a novel approach for the targeted construction of microbial cell factories.

Secondary Bubble Entrainment via Primary Bubble Bursting at a Viscoelastic Surface.

Bubble bursting at liquid surfaces is ubiquitous and plays a key role for the mass transfer across interfaces, impacting global climate and human health. Here, we document an unexpected phenomenon that when a bubble bursts at a viscoelastic surface of a bovine serum albumin solution, a secondary (daughter) bubble is entrapped with no subsequent jet drop ejection, contrary to the counterpart experimentally observed at a Newtonian surface. We show that the strong surface dilatational elastic stress from the viscoelastic surface retards the cavity collapse and efficiently damps out the precursor waves, thus facilitating the dominant wave focusing above the cavity nadir. The onset of daughter bubble entrainment is well predicted by an interfacial elastocapillary number comparing the effects of surface dilatational elasticity and surface tension. Our Letter highlights the important role of surface rheology on free surface flows and may find important implications in bubble dynamics with a contaminated interface exhibiting complex surface rheology.

ULBP2 is a biomarker related to prognosis and immunity in colon cancer.

The study aimed to determine whether ULBP2 was associated with prognosis and immune infiltration in colon cancer (CC) and provided important molecular basis in order to early non-invasive diagnosis and immunotherapy of CC. Using The Cancer Genome Atlas database (TCGA) and ImmPort database, we extracted messenger RNA (mRNA) data of CC and immune-related genes, then we used "limma" package, "survival" package, and Venn overlap analysis to obtain the differentially expressed mRNA (DEmRNA) associated with prognosis and immunity of CC patients. "pROC" package was used to analyze receiver operating characteristics (ROC) of target gene. We used chi-square test and two-class logistics model to identify clinicopathological parameters that correlated with target gene expression. In order to determine the effects of target gene expression and clinicopathological parameters on survival, univariate and multivariate cox regression analyses were performed. We analyzed the related functions and signaling pathways of target gene by enrichment analysis. Finally, the correlation between target gene and tumor immune infiltrating was explored by ssGSEA and spearman correlation analysis. Results showed that ULBP2 was a target gene associated with immunity and prognosis in CC patients. CC patients with higher ULBP2 expression had poor outcomes. In terms of ROC, ULBP2 had an area under the curve (AUC) of 0.984. ULBP2 was associated with T stage, N stage, and pathologic stage of CC patients, and served as an independent predictor of overall survival in CC patients. Functional enrichment analysis revealed ULBP2 was obviously enriched in pathways connected with carcinogenesis and immunosuppression. The expression of ULBP2 was significantly associated with tumor immune cells and immune checkpoints according to ssGSEA and spearman correlation analysis. To conclude, our study suggested that ULBP2 was associated with tumor immunity, and might be a biomarker associated with the diagnosis and prognosis of CC patients, and a potential target of CC immunotherapy.

Constructed the ceRNA network and predicted a FEZF1-AS1/miR-92b-3p/ZIC5 axis in colon cancer.

The purpose of this study was to identify the role of FEZF1-AS1 in colon cancer and predicted the underlying mechanism. We extracted sequencing data of colon cancer patients from The Cancer Genome Atlas database, identified the differential expression of long noncoding RNA, microRNA, and messenger RNA, constructed a competitive endogenous RNA network, and then analyzed prognosis. Then, we used the enrichment analysis databases for functional analysis. Finally, we studied the FEZF1-AS1/miR-92b-3p/ZIC5 axis. We detected the expression of FEZF1-AS1, miR-92b-3p, and ZIC5 via quantitative reverse transcription-PCR, transfected colon cancer cell RKO with lentivirus and conducted FEZF1-AS1 knockdown, and performed cancer-related functional assays. It indicated that many RNA in the competitive endogenous RNA network, such as ZIC5, were predicted to be related to overall survival of colon cancer patients, and enrichment analysis showed cancer-related signaling pathways, such as PI3K/AKT signaling pathway. The expression of FEZF1-AS1 and ZIC5 was significantly higher and that of miR-92b-3p was lower in the colon cancer than in the normal colon tissues. FEZF1-AS1 promoted the migration, proliferation, as well as invasion of RKO. According to the prediction, FEZF1-AS1 and ZIC5 might competitively bind to miR-92b-3p, leading to the weakening of the inhibitory impact of miR-92b-3p on ZIC5 and increasing expression of ZIC5, thus further activating the PI3K/AKT signaling pathway, which led to the occurrence and development of colon cancer. The study suggested that FEZF1-AS1 might be an effective diagnosis biomarker for colon cancer.

Enhancing the photocatalytic efficiency of two-dimensional aluminum nitride materials through strategic rare earth doping.

Two-dimensional (2D) materials demonstrate promising potential as high-efficiency photocatalysts. However, the intrinsic limitations of aluminum nitride (AlN), such as inadequate oxidation capacity, a high carrier recombination rate, and limited absorption of visible light, pose considerable challenges. In this paper, we introduce a novel co-doping technique with dysprosium (Dy) and carbon (C) on a 2D AlN monolayer, aiming to enhance its photocatalytic properties. Our first-principles calculations reveal a reduction in the bandgap and a significant enhancement in the visible light absorption rate of the co-doped Al24N22DyC2 structure. Notably, the distribution of the highest occupied molecular orbital and the lowest unoccupied molecular in proximity to Dy atoms demonstrates favorable conditions for carrier separation. Theoretical assessments of the hydrogen evolution reaction and oxygen evolution reaction activities further corroborate the potential of Al24N22DyC2 as a competent catalyst for photocatalytic reactions. These findings provide valuable theoretical insights for the experimental design and fabrication of novel, high-efficiency AlN semiconductor photocatalysts.

Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning.

In order to determine the polarizability and hyperpolarizability of a molecule, several key parameters need to be known, including the excitation energy of the ground and excited states, the transition dipole moment, and the difference of dipole moment between the ground and excited states. In this study, a machine-learning model was developed and trained to predict the molecular polarizability and second-order hyperpolarizability on a subset of QM9 data set. The density of states was employed as input to the model. The results demonstrated that the machine-learning model effectively estimated both polarizability and the order of magnitude of second-order hyperpolarizability. However, the model was unable to predict the dipole moment and first-order hyperpolarizability, suggesting limitations in its ability to predict the difference of dipole moment between the ground and excited states. The computational efficiency of machine-learning models compared to traditional quantum mechanical calculations enables the possibility of large-scale screening of molecules that satisfy specific requirements using existing databases. This work presents a potential solution for the efficient exploration and analysis of molecules on a larger scale.

Soil organic carbon regulates CH4 production through methanogenic evenness and available phosphorus under different straw managements.

Methane (CH4) is the main greenhouse gas emitted from rice paddy fields driven by methanogens, for which methanogenic abundance on CH4 production has been intensively investigated. However, information is limited about the relationship between methanogenic diversity (e.g., richness and evenness) and CH4 production. Three independent field experiments with different straw managements including returning method, burial depth, and burial amount were used to identify the effects of methanogenic diversity on CH4 production, and its regulating factors from soil properties in a rice-wheat cropping system. The results showed that methanogenic evenness (dominance) can explain 23% of variations in CH4 production potential. CH4 production potential was positively related to methanogenic evenness (R2 = 0.310, p < 0.001), which is driven by soil organic carbon (SOC), available phosphorus (AP), and nitrate (NO3-) through structure equation model (SEM). These findings indicate that methanogenic evenness has a critical role in evaluating the responses of CH4 production to agricultural practices following changes in soil properties. The SEM also revealed that SOC concentration influenced CH4 production potential indirectly via complementarity of methanogenic evenness (dominance) and available phosphorus (AP). Increasing SOC accumulation improved AP release and stimulated CH4 production when SOC was at a low level, whereas decreased evenness and suppressed CH4 production when SOC was at a high level. A nonlinear relationship was detected between SOC and CH4 production potential, and CH4 production potential decreased when SOC was ≥14.16 g kg-1. Our results indicated that the higher SOC sequestration can not only mitigate CO2 emissions directly but CH4 emissions indirectly, highlighting the importance to enhance SOC sequestration using optimum agricultural practices in a rice-wheat cropping system.

Laser doppler flowmetry to detect pulp vitality, clinical reference range and coincidence rate for pulpal blood flow in permanent maxillary incisors in Chinese children: a clinical study.

BACKGROUND: A laser doppler flowmetry (LDF) test can reflect the pulp vitality caused by the change in pulp blood flow (PBF). This study aimed to investigate the PBF of the permanent maxillary incisors using LDF and to calculate the clinical reference range and coincidence rate for pulp vitality using PBF as an indicator. METHODS: School-age children (7-12 years) were recruited randomly. A total of 455 children (216 female and 239 male) were included in this study. An additional 395 children (7-12 years) who attended the department due to anterior tooth trauma from October 2015 to February 2018 were included to assess the clinical occurrence rate. The PBF was measured using LDF equipment and an LDF probe. RESULTS: The clinical reference range of PBF values for the permanent maxillary incisors (teeth 11, 12, 21, and 22) in children were from 7 to 14 perfusion units (PU), 11 (6.016; 11.900 PU), 12 (6.677; 14.129 PU), 21 (6.043;11.899 PU), and 22 (6.668; 14.174 PU). There was a statistically significant correlation between PBF and children's age (p < 0.000) without any significant gender discrimination (p = 0.395). For all incisors, for any age group, the PBF detection value of the lateral incisors was significantly higher than that of the central incisors (p < 0.05). The clinical coincidence rate of detecting PBF in the traumatic teeth was 90.42% and the sensitivity and specificity were 36.99% and 99.88%, respectively. CONCLUSIONS: The determination of the PBF clinical reference range and clinical coincidence rate for the permanent maxillary incisors in children using LDF provided a promising theoretical basis for clinical applications.